{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:29:55.502714Z",
     "start_time": "2017-07-23T18:29:47.268362Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option(\"display.max_rows\",15)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:27.695466Z",
     "start_time": "2017-07-16T16:56:17.674240Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class dataset:\n",
    "    col_names = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\n",
    "        \"dst_bytes\",\"land\",\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\n",
    "        \"logged_in\",\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\n",
    "        \"num_file_creations\",\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\n",
    "        \"is_host_login\",\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\",\n",
    "        \"srv_serror_rate\",\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\",\n",
    "        \"diff_srv_rate\",\"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\n",
    "        \"dst_host_same_srv_rate\",\"dst_host_diff_srv_rate\",\"dst_host_same_src_port_rate\",\n",
    "        \"dst_host_srv_diff_host_rate\",\"dst_host_serror_rate\",\"dst_host_srv_serror_rate\",\n",
    "        \"dst_host_rerror_rate\",\"dst_host_srv_rerror_rate\",\"label\", \"difficulty_level\"]\n",
    "\n",
    "    kdd_train = pd.read_csv(\"dataset/KDDTrain+.txt\",names = col_names,)\n",
    "    kdd_test = pd.read_csv(\"dataset/KDDTest+.txt\",names = col_names,)\n",
    "    \n",
    "    kdd_train_ = pd.read_csv(\"dataset/KDDTrain+_20Percent.txt\",names = col_names,)\n",
    "    kdd_test_ = pd.read_csv(\"dataset/KDDTest-21.txt\",names = col_names,)\n",
    "    \n",
    "\n",
    "    kdd_diff_level_train = kdd_train[\"difficulty_level\"].copy()\n",
    "    kdd_diff_level_test = kdd_test[\"difficulty_level\"].copy()\n",
    "    \n",
    "    kdd_train = kdd_train.drop(\"difficulty_level\", axis = 1)\n",
    "    kdd_test = kdd_test.drop(\"difficulty_level\", axis = 1)\n",
    "    \n",
    "    kdd_train_ = kdd_train_.drop(\"difficulty_level\", axis = 1) #labels ['difficulty_level'] not contained in axis\n",
    "\n",
    "    kdd_test_ = kdd_test_.drop(\"difficulty_level\", axis = 1)\n",
    "    \n",
    "    kdd_train.to_csv(\"dataset/KDDTrain+.csv\")\n",
    "    kdd_test.to_csv(\"dataset/KDDTest+.csv\")\n",
    "    \n",
    "    kdd_train_.to_csv(\"dataset/KDDTrain_.csv\")\n",
    "    kdd_test_.to_csv(\"dataset/KDDTest_.csv\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:27.783425Z",
     "start_time": "2017-07-16T16:56:27.697150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Categories for protocol_type are 3\n",
      "Categories for protocol_type are Index(['icmp', 'tcp', 'udp'], dtype='object') \n",
      "\n",
      "Length of Categories for service are 70\n",
      "Categories for service are Index(['IRC', 'X11', 'Z39_50', 'aol', 'auth', 'bgp', 'courier', 'csnet_ns',\n",
      "       'ctf', 'daytime', 'discard', 'domain', 'domain_u', 'echo', 'eco_i',\n",
      "       'ecr_i', 'efs', 'exec', 'finger', 'ftp', 'ftp_data', 'gopher',\n",
      "       'harvest', 'hostnames', 'http', 'http_2784', 'http_443', 'http_8001',\n",
      "       'imap4', 'iso_tsap', 'klogin', 'kshell', 'ldap', 'link', 'login', 'mtp',\n",
      "       'name', 'netbios_dgm', 'netbios_ns', 'netbios_ssn', 'netstat', 'nnsp',\n",
      "       'nntp', 'ntp_u', 'other', 'pm_dump', 'pop_2', 'pop_3', 'printer',\n",
      "       'private', 'red_i', 'remote_job', 'rje', 'shell', 'smtp', 'sql_net',\n",
      "       'ssh', 'sunrpc', 'supdup', 'systat', 'telnet', 'tftp_u', 'tim_i',\n",
      "       'time', 'urh_i', 'urp_i', 'uucp', 'uucp_path', 'vmnet', 'whois'],\n",
      "      dtype='object') \n",
      "\n",
      "Length of Categories for flag are 11\n",
      "Categories for flag are Index(['OTH', 'REJ', 'RSTO', 'RSTOS0', 'RSTR', 'S0', 'S1', 'S2', 'S3', 'SF',\n",
      "       'SH'],\n",
      "      dtype='object') \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/apcloud44/miniconda3/envs/p3/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  \"\"\"\n",
      "/home/apcloud44/miniconda3/envs/p3/lib/python3.6/site-packages/ipykernel_launcher.py:8: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  \n",
      "/home/apcloud44/miniconda3/envs/p3/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "category_variables = [\"protocol_type\",\"service\",\"flag\"]\n",
    "for cv in category_variables:\n",
    "    dataset.kdd_train[cv] = dataset.kdd_train[cv].astype(\"category\")\n",
    "    dataset.kdd_test[cv] = dataset.kdd_test[cv].astype(\"category\", \n",
    "                                                       categories = dataset.kdd_train[cv].cat.categories)\n",
    "    \n",
    "    dataset.kdd_train_[cv] = dataset.kdd_train_[cv].astype(\"category\", \n",
    "                                                       categories = dataset.kdd_train[cv].cat.categories)\n",
    "    dataset.kdd_test_[cv] = dataset.kdd_test_[cv].astype(\"category\", \n",
    "                                                       categories = dataset.kdd_train[cv].cat.categories)\n",
    "    \n",
    "    print(\"Length of Categories for {} are {}\".format(cv , len(dataset.kdd_train[cv].cat.categories)))\n",
    "    print(\"Categories for {} are {} \\n\".format(cv ,dataset.kdd_train[cv].cat.categories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:27.891600Z",
     "start_time": "2017-07-16T16:56:27.785114Z"
    }
   },
   "outputs": [
    {
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       "      <th>duration</th>\n",
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       "      <td>0.00</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>neptune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125972</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125973 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
       "0              0           tcp  ftp_data   SF        491          0     0   \n",
       "1              0           udp     other   SF        146          0     0   \n",
       "2              0           tcp   private   S0          0          0     0   \n",
       "3              0           tcp      http   SF        232       8153     0   \n",
       "4              0           tcp      http   SF        199        420     0   \n",
       "5              0           tcp   private  REJ          0          0     0   \n",
       "6              0           tcp   private   S0          0          0     0   \n",
       "...          ...           ...       ...  ...        ...        ...   ...   \n",
       "125966         0           tcp   private   S0          0          0     0   \n",
       "125967         0           tcp      http   SF        359        375     0   \n",
       "125968         0           tcp   private   S0          0          0     0   \n",
       "125969         8           udp   private   SF        105        145     0   \n",
       "125970         0           tcp      smtp   SF       2231        384     0   \n",
       "125971         0           tcp    klogin   S0          0          0     0   \n",
       "125972         0           tcp  ftp_data   SF        151          0     0   \n",
       "\n",
       "        wrong_fragment  urgent  hot   ...     dst_host_srv_count  \\\n",
       "0                    0       0    0   ...                     25   \n",
       "1                    0       0    0   ...                      1   \n",
       "2                    0       0    0   ...                     26   \n",
       "3                    0       0    0   ...                    255   \n",
       "4                    0       0    0   ...                    255   \n",
       "5                    0       0    0   ...                     19   \n",
       "6                    0       0    0   ...                      9   \n",
       "...                ...     ...  ...   ...                    ...   \n",
       "125966               0       0    0   ...                     13   \n",
       "125967               0       0    0   ...                    255   \n",
       "125968               0       0    0   ...                     25   \n",
       "125969               0       0    0   ...                    244   \n",
       "125970               0       0    0   ...                     30   \n",
       "125971               0       0    0   ...                      8   \n",
       "125972               0       0    0   ...                     77   \n",
       "\n",
       "        dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                         0.17                    0.03   \n",
       "1                         0.00                    0.60   \n",
       "2                         0.10                    0.05   \n",
       "3                         1.00                    0.00   \n",
       "4                         1.00                    0.00   \n",
       "5                         0.07                    0.07   \n",
       "6                         0.04                    0.05   \n",
       "...                        ...                     ...   \n",
       "125966                    0.05                    0.07   \n",
       "125967                    1.00                    0.00   \n",
       "125968                    0.10                    0.06   \n",
       "125969                    0.96                    0.01   \n",
       "125970                    0.12                    0.06   \n",
       "125971                    0.03                    0.05   \n",
       "125972                    0.30                    0.03   \n",
       "\n",
       "        dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                              0.17                         0.00   \n",
       "1                              0.88                         0.00   \n",
       "2                              0.00                         0.00   \n",
       "3                              0.03                         0.04   \n",
       "4                              0.00                         0.00   \n",
       "5                              0.00                         0.00   \n",
       "6                              0.00                         0.00   \n",
       "...                             ...                          ...   \n",
       "125966                         0.00                         0.00   \n",
       "125967                         0.33                         0.04   \n",
       "125968                         0.00                         0.00   \n",
       "125969                         0.01                         0.00   \n",
       "125970                         0.00                         0.00   \n",
       "125971                         0.00                         0.00   \n",
       "125972                         0.30                         0.00   \n",
       "\n",
       "        dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                       0.00                      0.00                  0.05   \n",
       "1                       0.00                      0.00                  0.00   \n",
       "2                       1.00                      1.00                  0.00   \n",
       "3                       0.03                      0.01                  0.00   \n",
       "4                       0.00                      0.00                  0.00   \n",
       "5                       0.00                      0.00                  1.00   \n",
       "6                       1.00                      1.00                  0.00   \n",
       "...                      ...                       ...                   ...   \n",
       "125966                  1.00                      1.00                  0.00   \n",
       "125967                  0.33                      0.00                  0.00   \n",
       "125968                  1.00                      1.00                  0.00   \n",
       "125969                  0.00                      0.00                  0.00   \n",
       "125970                  0.72                      0.00                  0.01   \n",
       "125971                  1.00                      1.00                  0.00   \n",
       "125972                  0.00                      0.00                  0.00   \n",
       "\n",
       "        dst_host_srv_rerror_rate    label  \n",
       "0                           0.00   normal  \n",
       "1                           0.00   normal  \n",
       "2                           0.00  neptune  \n",
       "3                           0.01   normal  \n",
       "4                           0.00   normal  \n",
       "5                           1.00  neptune  \n",
       "6                           0.00  neptune  \n",
       "...                          ...      ...  \n",
       "125966                      0.00  neptune  \n",
       "125967                      0.00   normal  \n",
       "125968                      0.00  neptune  \n",
       "125969                      0.00   normal  \n",
       "125970                      0.00   normal  \n",
       "125971                      0.00  neptune  \n",
       "125972                      0.00   normal  \n",
       "\n",
       "[125973 rows x 42 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.kdd_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:27.943045Z",
     "start_time": "2017-07-16T16:56:27.893361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>REJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>neptune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>REJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>neptune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>12983</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>eco_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>saint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>tcp</td>\n",
       "      <td>telnet</td>\n",
       "      <td>RSTO</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.71</td>\n",
       "      <td>mscan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>267</td>\n",
       "      <td>14515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>smtp</td>\n",
       "      <td>SF</td>\n",
       "      <td>1022</td>\n",
       "      <td>387</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.04</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22537</th>\n",
       "      <td>1</td>\n",
       "      <td>tcp</td>\n",
       "      <td>smtp</td>\n",
       "      <td>SF</td>\n",
       "      <td>2599</td>\n",
       "      <td>293</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>186</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>mailbomb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22538</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>1032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>smurf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22539</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>smtp</td>\n",
       "      <td>SF</td>\n",
       "      <td>794</td>\n",
       "      <td>333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22540</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>317</td>\n",
       "      <td>938</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22541</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>54540</td>\n",
       "      <td>8314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22542</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>domain_u</td>\n",
       "      <td>SF</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>252</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22543</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>sunrpc</td>\n",
       "      <td>REJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.00</td>\n",
       "      <td>mscan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22544 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration protocol_type   service  flag  src_bytes  dst_bytes  land  \\\n",
       "0             0           tcp   private   REJ          0          0     0   \n",
       "1             0           tcp   private   REJ          0          0     0   \n",
       "2             2           tcp  ftp_data    SF      12983          0     0   \n",
       "3             0          icmp     eco_i    SF         20          0     0   \n",
       "4             1           tcp    telnet  RSTO          0         15     0   \n",
       "5             0           tcp      http    SF        267      14515     0   \n",
       "6             0           tcp      smtp    SF       1022        387     0   \n",
       "...         ...           ...       ...   ...        ...        ...   ...   \n",
       "22537         1           tcp      smtp    SF       2599        293     0   \n",
       "22538         0          icmp     ecr_i    SF       1032          0     0   \n",
       "22539         0           tcp      smtp    SF        794        333     0   \n",
       "22540         0           tcp      http    SF        317        938     0   \n",
       "22541         0           tcp      http    SF      54540       8314     0   \n",
       "22542         0           udp  domain_u    SF         42         42     0   \n",
       "22543         0           tcp    sunrpc   REJ          0          0     0   \n",
       "\n",
       "       wrong_fragment  urgent  hot    ...     dst_host_srv_count  \\\n",
       "0                   0       0    0    ...                     10   \n",
       "1                   0       0    0    ...                      1   \n",
       "2                   0       0    0    ...                     86   \n",
       "3                   0       0    0    ...                     57   \n",
       "4                   0       0    0    ...                     86   \n",
       "5                   0       0    0    ...                    255   \n",
       "6                   0       0    0    ...                     28   \n",
       "...               ...     ...  ...    ...                    ...   \n",
       "22537               0       0    0    ...                    186   \n",
       "22538               0       0    0    ...                    255   \n",
       "22539               0       0    0    ...                    141   \n",
       "22540               0       0    0    ...                    255   \n",
       "22541               0       0    2    ...                    255   \n",
       "22542               0       0    0    ...                    252   \n",
       "22543               0       0    0    ...                     21   \n",
       "\n",
       "       dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                        0.04                    0.06   \n",
       "1                        0.00                    0.06   \n",
       "2                        0.61                    0.04   \n",
       "3                        1.00                    0.00   \n",
       "4                        0.31                    0.17   \n",
       "5                        1.00                    0.00   \n",
       "6                        0.11                    0.72   \n",
       "...                       ...                     ...   \n",
       "22537                    0.73                    0.13   \n",
       "22538                    1.00                    0.00   \n",
       "22539                    0.72                    0.06   \n",
       "22540                    1.00                    0.00   \n",
       "22541                    1.00                    0.00   \n",
       "22542                    0.99                    0.01   \n",
       "22543                    0.08                    0.03   \n",
       "\n",
       "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                             0.00                         0.00   \n",
       "1                             0.00                         0.00   \n",
       "2                             0.61                         0.02   \n",
       "3                             1.00                         0.28   \n",
       "4                             0.03                         0.02   \n",
       "5                             0.01                         0.03   \n",
       "6                             0.00                         0.00   \n",
       "...                            ...                          ...   \n",
       "22537                         0.00                         0.00   \n",
       "22538                         1.00                         0.00   \n",
       "22539                         0.01                         0.01   \n",
       "22540                         0.01                         0.01   \n",
       "22541                         0.00                         0.00   \n",
       "22542                         0.00                         0.00   \n",
       "22543                         0.00                         0.00   \n",
       "\n",
       "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                      0.00                       0.0                  1.00   \n",
       "1                      0.00                       0.0                  1.00   \n",
       "2                      0.00                       0.0                  0.00   \n",
       "3                      0.00                       0.0                  0.00   \n",
       "4                      0.00                       0.0                  0.83   \n",
       "5                      0.01                       0.0                  0.00   \n",
       "6                      0.00                       0.0                  0.72   \n",
       "...                     ...                       ...                   ...   \n",
       "22537                  0.00                       0.0                  0.26   \n",
       "22538                  0.00                       0.0                  0.00   \n",
       "22539                  0.01                       0.0                  0.00   \n",
       "22540                  0.01                       0.0                  0.00   \n",
       "22541                  0.00                       0.0                  0.07   \n",
       "22542                  0.00                       0.0                  0.00   \n",
       "22543                  0.00                       0.0                  0.44   \n",
       "\n",
       "       dst_host_srv_rerror_rate     label  \n",
       "0                          1.00   neptune  \n",
       "1                          1.00   neptune  \n",
       "2                          0.00    normal  \n",
       "3                          0.00     saint  \n",
       "4                          0.71     mscan  \n",
       "5                          0.00    normal  \n",
       "6                          0.04    normal  \n",
       "...                         ...       ...  \n",
       "22537                      0.00  mailbomb  \n",
       "22538                      0.00     smurf  \n",
       "22539                      0.00    normal  \n",
       "22540                      0.00    normal  \n",
       "22541                      0.07      back  \n",
       "22542                      0.00    normal  \n",
       "22543                      1.00     mscan  \n",
       "\n",
       "[22544 rows x 42 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.kdd_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:28.382954Z",
     "start_time": "2017-07-16T16:56:27.944743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>125973.00000</td>\n",
       "      <td>1.259730e+05</td>\n",
       "      <td>1.259730e+05</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>287.14465</td>\n",
       "      <td>4.556674e+04</td>\n",
       "      <td>1.977911e+04</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.022687</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.204409</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.395736</td>\n",
       "      <td>0.279250</td>\n",
       "      <td>...</td>\n",
       "      <td>182.148945</td>\n",
       "      <td>115.653005</td>\n",
       "      <td>0.521242</td>\n",
       "      <td>0.082951</td>\n",
       "      <td>0.148379</td>\n",
       "      <td>0.032542</td>\n",
       "      <td>0.284452</td>\n",
       "      <td>0.278485</td>\n",
       "      <td>0.118832</td>\n",
       "      <td>0.120240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2604.51531</td>\n",
       "      <td>5.870331e+06</td>\n",
       "      <td>4.021269e+06</td>\n",
       "      <td>0.014086</td>\n",
       "      <td>0.253530</td>\n",
       "      <td>0.014366</td>\n",
       "      <td>2.149968</td>\n",
       "      <td>0.045239</td>\n",
       "      <td>0.489010</td>\n",
       "      <td>23.942042</td>\n",
       "      <td>...</td>\n",
       "      <td>99.206213</td>\n",
       "      <td>110.702741</td>\n",
       "      <td>0.448949</td>\n",
       "      <td>0.188922</td>\n",
       "      <td>0.308997</td>\n",
       "      <td>0.112564</td>\n",
       "      <td>0.444784</td>\n",
       "      <td>0.445669</td>\n",
       "      <td>0.306557</td>\n",
       "      <td>0.319459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.760000e+02</td>\n",
       "      <td>5.160000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42908.00000</td>\n",
       "      <td>1.379964e+09</td>\n",
       "      <td>1.309937e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7479.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           duration     src_bytes     dst_bytes           land  \\\n",
       "count  125973.00000  1.259730e+05  1.259730e+05  125973.000000   \n",
       "mean      287.14465  4.556674e+04  1.977911e+04       0.000198   \n",
       "std      2604.51531  5.870331e+06  4.021269e+06       0.014086   \n",
       "min         0.00000  0.000000e+00  0.000000e+00       0.000000   \n",
       "25%         0.00000  0.000000e+00  0.000000e+00       0.000000   \n",
       "50%         0.00000  4.400000e+01  0.000000e+00       0.000000   \n",
       "75%         0.00000  2.760000e+02  5.160000e+02       0.000000   \n",
       "max     42908.00000  1.379964e+09  1.309937e+09       1.000000   \n",
       "\n",
       "       wrong_fragment         urgent            hot  num_failed_logins  \\\n",
       "count   125973.000000  125973.000000  125973.000000      125973.000000   \n",
       "mean         0.022687       0.000111       0.204409           0.001222   \n",
       "std          0.253530       0.014366       2.149968           0.045239   \n",
       "min          0.000000       0.000000       0.000000           0.000000   \n",
       "25%          0.000000       0.000000       0.000000           0.000000   \n",
       "50%          0.000000       0.000000       0.000000           0.000000   \n",
       "75%          0.000000       0.000000       0.000000           0.000000   \n",
       "max          3.000000       3.000000      77.000000           5.000000   \n",
       "\n",
       "           logged_in  num_compromised            ...             \\\n",
       "count  125973.000000    125973.000000            ...              \n",
       "mean        0.395736         0.279250            ...              \n",
       "std         0.489010        23.942042            ...              \n",
       "min         0.000000         0.000000            ...              \n",
       "25%         0.000000         0.000000            ...              \n",
       "50%         0.000000         0.000000            ...              \n",
       "75%         1.000000         0.000000            ...              \n",
       "max         1.000000      7479.000000            ...              \n",
       "\n",
       "       dst_host_count  dst_host_srv_count  dst_host_same_srv_rate  \\\n",
       "count   125973.000000       125973.000000           125973.000000   \n",
       "mean       182.148945          115.653005                0.521242   \n",
       "std         99.206213          110.702741                0.448949   \n",
       "min          0.000000            0.000000                0.000000   \n",
       "25%         82.000000           10.000000                0.050000   \n",
       "50%        255.000000           63.000000                0.510000   \n",
       "75%        255.000000          255.000000                1.000000   \n",
       "max        255.000000          255.000000                1.000000   \n",
       "\n",
       "       dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "count           125973.000000                125973.000000   \n",
       "mean                 0.082951                     0.148379   \n",
       "std                  0.188922                     0.308997   \n",
       "min                  0.000000                     0.000000   \n",
       "25%                  0.000000                     0.000000   \n",
       "50%                  0.020000                     0.000000   \n",
       "75%                  0.070000                     0.060000   \n",
       "max                  1.000000                     1.000000   \n",
       "\n",
       "       dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "count                125973.000000         125973.000000   \n",
       "mean                      0.032542              0.284452   \n",
       "std                       0.112564              0.444784   \n",
       "min                       0.000000              0.000000   \n",
       "25%                       0.000000              0.000000   \n",
       "50%                       0.000000              0.000000   \n",
       "75%                       0.020000              1.000000   \n",
       "max                       1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "count             125973.000000         125973.000000   \n",
       "mean                   0.278485              0.118832   \n",
       "std                    0.445669              0.306557   \n",
       "min                    0.000000              0.000000   \n",
       "25%                    0.000000              0.000000   \n",
       "50%                    0.000000              0.000000   \n",
       "75%                    1.000000              0.000000   \n",
       "max                    1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_rerror_rate  \n",
       "count             125973.000000  \n",
       "mean                   0.120240  \n",
       "std                    0.319459  \n",
       "min                    0.000000  \n",
       "25%                    0.000000  \n",
       "50%                    0.000000  \n",
       "75%                    0.000000  \n",
       "max                    1.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.kdd_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zero Data Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:28.907330Z",
     "start_time": "2017-07-16T16:56:28.384781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6318702836170865"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = dataset.kdd_train.isin([0])\n",
    "a.sum().sum() / a.size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:39.031622Z",
     "start_time": "2017-07-16T16:56:19.741Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22544.000000</td>\n",
       "      <td>2.254400e+04</td>\n",
       "      <td>2.254400e+04</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "      <td>22544.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>218.859076</td>\n",
       "      <td>1.039545e+04</td>\n",
       "      <td>2.056019e+03</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.008428</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.105394</td>\n",
       "      <td>0.021647</td>\n",
       "      <td>0.442202</td>\n",
       "      <td>0.119899</td>\n",
       "      <td>...</td>\n",
       "      <td>193.869411</td>\n",
       "      <td>140.750532</td>\n",
       "      <td>0.608722</td>\n",
       "      <td>0.090540</td>\n",
       "      <td>0.132261</td>\n",
       "      <td>0.019638</td>\n",
       "      <td>0.097814</td>\n",
       "      <td>0.099426</td>\n",
       "      <td>0.233385</td>\n",
       "      <td>0.226683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1407.176612</td>\n",
       "      <td>4.727864e+05</td>\n",
       "      <td>2.121930e+04</td>\n",
       "      <td>0.017619</td>\n",
       "      <td>0.142599</td>\n",
       "      <td>0.036473</td>\n",
       "      <td>0.928428</td>\n",
       "      <td>0.150328</td>\n",
       "      <td>0.496659</td>\n",
       "      <td>7.269597</td>\n",
       "      <td>...</td>\n",
       "      <td>94.035663</td>\n",
       "      <td>111.783972</td>\n",
       "      <td>0.435688</td>\n",
       "      <td>0.220717</td>\n",
       "      <td>0.306268</td>\n",
       "      <td>0.085394</td>\n",
       "      <td>0.273139</td>\n",
       "      <td>0.281866</td>\n",
       "      <td>0.387229</td>\n",
       "      <td>0.400875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.870000e+02</td>\n",
       "      <td>6.010000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>57715.000000</td>\n",
       "      <td>6.282565e+07</td>\n",
       "      <td>1.345927e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           duration     src_bytes     dst_bytes          land  wrong_fragment  \\\n",
       "count  22544.000000  2.254400e+04  2.254400e+04  22544.000000    22544.000000   \n",
       "mean     218.859076  1.039545e+04  2.056019e+03      0.000311        0.008428   \n",
       "std     1407.176612  4.727864e+05  2.121930e+04      0.017619        0.142599   \n",
       "min        0.000000  0.000000e+00  0.000000e+00      0.000000        0.000000   \n",
       "25%        0.000000  0.000000e+00  0.000000e+00      0.000000        0.000000   \n",
       "50%        0.000000  5.400000e+01  4.600000e+01      0.000000        0.000000   \n",
       "75%        0.000000  2.870000e+02  6.010000e+02      0.000000        0.000000   \n",
       "max    57715.000000  6.282565e+07  1.345927e+06      1.000000        3.000000   \n",
       "\n",
       "             urgent           hot  num_failed_logins     logged_in  \\\n",
       "count  22544.000000  22544.000000       22544.000000  22544.000000   \n",
       "mean       0.000710      0.105394           0.021647      0.442202   \n",
       "std        0.036473      0.928428           0.150328      0.496659   \n",
       "min        0.000000      0.000000           0.000000      0.000000   \n",
       "25%        0.000000      0.000000           0.000000      0.000000   \n",
       "50%        0.000000      0.000000           0.000000      0.000000   \n",
       "75%        0.000000      0.000000           0.000000      1.000000   \n",
       "max        3.000000    101.000000           4.000000      1.000000   \n",
       "\n",
       "       num_compromised            ...             dst_host_count  \\\n",
       "count     22544.000000            ...               22544.000000   \n",
       "mean          0.119899            ...                 193.869411   \n",
       "std           7.269597            ...                  94.035663   \n",
       "min           0.000000            ...                   0.000000   \n",
       "25%           0.000000            ...                 121.000000   \n",
       "50%           0.000000            ...                 255.000000   \n",
       "75%           0.000000            ...                 255.000000   \n",
       "max         796.000000            ...                 255.000000   \n",
       "\n",
       "       dst_host_srv_count  dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "count        22544.000000            22544.000000            22544.000000   \n",
       "mean           140.750532                0.608722                0.090540   \n",
       "std            111.783972                0.435688                0.220717   \n",
       "min              0.000000                0.000000                0.000000   \n",
       "25%             15.000000                0.070000                0.000000   \n",
       "50%            168.000000                0.920000                0.010000   \n",
       "75%            255.000000                1.000000                0.060000   \n",
       "max            255.000000                1.000000                1.000000   \n",
       "\n",
       "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "count                 22544.000000                 22544.000000   \n",
       "mean                      0.132261                     0.019638   \n",
       "std                       0.306268                     0.085394   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       0.000000                     0.000000   \n",
       "50%                       0.000000                     0.000000   \n",
       "75%                       0.030000                     0.010000   \n",
       "max                       1.000000                     1.000000   \n",
       "\n",
       "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "count          22544.000000              22544.000000          22544.000000   \n",
       "mean               0.097814                  0.099426              0.233385   \n",
       "std                0.273139                  0.281866              0.387229   \n",
       "min                0.000000                  0.000000              0.000000   \n",
       "25%                0.000000                  0.000000              0.000000   \n",
       "50%                0.000000                  0.000000              0.000000   \n",
       "75%                0.000000                  0.000000              0.360000   \n",
       "max                1.000000                  1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_rerror_rate  \n",
       "count              22544.000000  \n",
       "mean                   0.226683  \n",
       "std                    0.400875  \n",
       "min                    0.000000  \n",
       "25%                    0.000000  \n",
       "50%                    0.000000  \n",
       "75%                    0.170000  \n",
       "max                    1.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.kdd_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-16T16:56:39.032577Z",
     "start_time": "2017-07-16T16:56:21.584Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column - Label\n",
      "Unique values: \n",
      "0          normal\n",
      "1          normal\n",
      "2         neptune\n",
      "3          normal\n",
      "4          normal\n",
      "5         neptune\n",
      "6         neptune\n",
      "           ...   \n",
      "125966    neptune\n",
      "125967     normal\n",
      "125968    neptune\n",
      "125969     normal\n",
      "125970     normal\n",
      "125971    neptune\n",
      "125972     normal\n",
      "Name: label, Length: 125973, dtype: object\n",
      "\n",
      "Statistical properties: \n",
      "count     125973\n",
      "unique        23\n",
      "top       normal\n",
      "freq       67343\n",
      "Name: label, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Column - Label\")\n",
    "print(\"Unique values: \\n{}\".format(dataset.kdd_train.label))\n",
    "print(\"\\nStatistical properties: \\n{}\".format(dataset.kdd_train.label.describe()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.559564Z",
     "start_time": "2017-06-19T17:09:47.516046Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attack_types = {\n",
    "    'normal': 'normal',\n",
    "    \n",
    "    'back': 'DoS',\n",
    "    'land': 'DoS',\n",
    "    'neptune': 'DoS',\n",
    "    'pod': 'DoS',\n",
    "    'smurf': 'DoS',\n",
    "    'teardrop': 'DoS',\n",
    "    'mailbomb': 'DoS',\n",
    "    'apache2': 'DoS',\n",
    "    'processtable': 'DoS',\n",
    "    'udpstorm': 'DoS',\n",
    "    \n",
    "    'ipsweep': 'Probe',\n",
    "    'nmap': 'Probe',\n",
    "    'portsweep': 'Probe',\n",
    "    'satan': 'Probe',\n",
    "    'mscan': 'Probe',\n",
    "    'saint': 'Probe',\n",
    "\n",
    "    'ftp_write': 'R2L',\n",
    "    'guess_passwd': 'R2L',\n",
    "    'imap': 'R2L',\n",
    "    'multihop': 'R2L',\n",
    "    'phf': 'R2L',\n",
    "    'spy': 'R2L',\n",
    "    'warezclient': 'R2L',\n",
    "    'warezmaster': 'R2L',\n",
    "    'sendmail': 'R2L',\n",
    "    'named': 'R2L',\n",
    "    'snmpgetattack': 'R2L',\n",
    "    'snmpguess': 'R2L',\n",
    "    'xlock': 'R2L',\n",
    "    'xsnoop': 'R2L',\n",
    "    'worm': 'R2L',\n",
    "    \n",
    "    'buffer_overflow': 'U2R',\n",
    "    'loadmodule': 'U2R',\n",
    "    'perl': 'U2R',\n",
    "    'rootkit': 'U2R',\n",
    "    'httptunnel': 'U2R',\n",
    "    'ps': 'U2R',    \n",
    "    'sqlattack': 'U2R',\n",
    "    'xterm': 'U2R'\n",
    "}\n",
    "\n",
    "is_attack = {\n",
    "    \"DoS\":\"Attack\",\n",
    "    \"R2L\":\"Attack\",\n",
    "    \"U2R\":\"Attack\",\n",
    "    \"Probe\":\"Attack\",\n",
    "    \"normal\":\"Normal\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.722528Z",
     "start_time": "2017-06-19T17:09:47.561479Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset.kdd_train[\"type\"] = dataset.kdd_train.label.map(lambda x: attack_types[x])\n",
    "dataset.kdd_train[\"is\"] = dataset.kdd_train.type.map(lambda x: is_attack[x])\n",
    "\n",
    "dataset.kdd_test[\"type\"] = dataset.kdd_test.label.map(lambda x: attack_types[x])\n",
    "dataset.kdd_test[\"is\"] = dataset.kdd_test.type.map(lambda x: is_attack[x])\n",
    "\n",
    "dataset.kdd_train_[\"type\"] = dataset.kdd_train_.label.map(lambda x: attack_types[x])\n",
    "dataset.kdd_train_[\"is\"] = dataset.kdd_train_.type.map(lambda x: is_attack[x])\n",
    "\n",
    "dataset.kdd_test_[\"type\"] = dataset.kdd_test_.label.map(lambda x: attack_types[x])\n",
    "dataset.kdd_test_[\"is\"] = dataset.kdd_test_.type.map(lambda x: is_attack[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:59:02.920052Z",
     "start_time": "2017-06-19T17:59:02.224882Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1804888\n",
      "2895749\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6232888278645697"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = dataset.kdd_train.set_index(\"is\")\n",
    "\n",
    "print(a.loc[\"Normal\"].isin([0]).sum().sum())\n",
    "print(a.loc[\"Normal\"].size)\n",
    "\n",
    "\n",
    "a.loc[\"Normal\"].isin([0]).sum().sum() / a.loc[\"Normal\"].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:59:49.399768Z",
     "start_time": "2017-06-19T17:59:48.770746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1538253\n",
      "2521090\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.610153941350765"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = dataset.kdd_train.set_index(\"is\")\n",
    "\n",
    "print(a.loc[\"Attack\"].isin([0]).sum().sum())\n",
    "print(a.loc[\"Attack\"].size)\n",
    "\n",
    "\n",
    "a.loc[\"Attack\"].isin([0]).sum().sum() / a.loc[\"Attack\"].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T18:01:16.584899Z",
     "start_time": "2017-06-19T18:01:16.580586Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5398779172042101"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1804888 / (1804888 + 1538253)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.733213Z",
     "start_time": "2017-06-19T17:09:47.724616Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kdd_attack_type_group = dataset.kdd_train.groupby(\"type\")\n",
    "kdd_is_attack_group = dataset.kdd_train.groupby(\"is\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.757839Z",
     "start_time": "2017-06-19T17:09:47.735049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "DoS       45927\n",
       "Probe     11656\n",
       "R2L         995\n",
       "U2R          52\n",
       "normal    67343\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdd_attack_type_group.type.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.781988Z",
     "start_time": "2017-06-19T17:09:47.759999Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is\n",
       "Attack    58630\n",
       "Normal    67343\n",
       "Name: is, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdd_is_attack_group[\"is\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7fd2b2eda1d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdd_attack_type_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.832823Z",
     "start_time": "2017-06-19T17:09:47.783982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['neptune', 'warezclient', 'ipsweep', 'portsweep', 'teardrop',\n",
       "       'nmap', 'satan', 'smurf', 'pod', 'back', 'guess_passwd',\n",
       "       'ftp_write', 'multihop', 'rootkit', 'buffer_overflow', 'imap',\n",
       "       'warezmaster', 'phf', 'land', 'loadmodule', 'spy', 'perl'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dataset.kdd_train.set_index(\"is\")\n",
    "df.loc[\"Attack\"].label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.864708Z",
     "start_time": "2017-06-19T17:09:47.837029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['normal'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Normal\"].label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.869772Z",
     "start_time": "2017-06-19T17:09:47.866539Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#kdd_is_attack_group.hist(figsize=[25,22])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.879868Z",
     "start_time": "2017-06-19T17:09:47.872375Z"
    },
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#kdd_attack_type_group.hist(figsize=[25,22])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.911753Z",
     "start_time": "2017-06-19T17:09:47.882175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "difficulty_level\n",
       "0      0.052392\n",
       "1      0.049217\n",
       "2      0.042866\n",
       "3      0.051598\n",
       "4      0.062712\n",
       "5      0.064299\n",
       "6      0.076207\n",
       "        ...    \n",
       "15     3.167345\n",
       "16     1.899613\n",
       "17     2.440205\n",
       "18    16.405897\n",
       "19     8.163654\n",
       "20    15.351702\n",
       "21    49.659054\n",
       "Name: difficulty_level, Length: 22, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = dataset.kdd_diff_level_train.groupby(dataset.kdd_diff_level_train)\n",
    "(gb.count() / dataset.kdd_diff_level_train.count())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:47.930966Z",
     "start_time": "2017-06-19T17:09:47.914585Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "difficulty_level\n",
       "0      0.545600\n",
       "1      0.385912\n",
       "2      0.243967\n",
       "3      0.514549\n",
       "4      0.448013\n",
       "5      0.456884\n",
       "6      0.696416\n",
       "        ...    \n",
       "15     5.216466\n",
       "16     3.020759\n",
       "17     5.180979\n",
       "18    13.160930\n",
       "19     3.947835\n",
       "20     5.957239\n",
       "21    47.436125\n",
       "Name: difficulty_level, Length: 22, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = dataset.kdd_diff_level_test.groupby(dataset.kdd_diff_level_test)\n",
    "(gb.count() / dataset.kdd_diff_level_test.count())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.841770Z",
     "start_time": "2017-06-19T17:09:47.933136Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummy_variables_2labels = [*category_variables, \"is\"]\n",
    "dummy_variables_5labels = [*category_variables, \"type\"]\n",
    "attack_codes_2labels = {\"Attack\":1, \"Normal\":0}\n",
    "attack_codes_5labels = {'DoS':1, 'normal':0, 'Probe':2, 'R2L':3, 'U2R':4}\n",
    "    \n",
    "class preprocessing:\n",
    "    kdd_train_2labels = pd.get_dummies(dataset.kdd_train, columns = dummy_variables_2labels, prefix=dummy_variables_2labels)\n",
    "    kdd_train_5labels = pd.get_dummies(dataset.kdd_train, columns = dummy_variables_5labels, prefix=dummy_variables_5labels)\n",
    "\n",
    "    kdd_test_2labels = pd.get_dummies(dataset.kdd_test, columns = dummy_variables_2labels, prefix=dummy_variables_2labels)\n",
    "    kdd_test_5labels = pd.get_dummies(dataset.kdd_test, columns = dummy_variables_5labels, prefix=dummy_variables_5labels)\n",
    "\n",
    "    kdd_train__2labels = pd.get_dummies(dataset.kdd_train_, columns = dummy_variables_2labels, prefix=dummy_variables_2labels)\n",
    "    kdd_train__5labels = pd.get_dummies(dataset.kdd_train_, columns = dummy_variables_5labels, prefix=dummy_variables_5labels)\n",
    "        \n",
    "    kdd_test__2labels = pd.get_dummies(dataset.kdd_test_, columns = dummy_variables_2labels, prefix=dummy_variables_2labels)\n",
    "    kdd_test__5labels = pd.get_dummies(dataset.kdd_test_, columns = dummy_variables_5labels, prefix=dummy_variables_5labels)\n",
    "\n",
    "\n",
    "    kdd_train_2labels_y = dataset.kdd_train[\"is\"].copy() # For SVM\n",
    "    kdd_train_5labels_y = dataset.kdd_train[\"type\"].copy() # For SVM\n",
    "\n",
    "    kdd_test_2labels_y = dataset.kdd_test[\"is\"].copy() # For SVM\n",
    "    kdd_test_5labels_y = dataset.kdd_test[\"type\"].copy() # For SVM\n",
    "    \n",
    "    kdd_train__2labels_y = dataset.kdd_train_[\"is\"].copy() # For SVM\n",
    "    kdd_train__5labels_y = dataset.kdd_train_[\"type\"].copy() # For SVM\n",
    "    \n",
    "    kdd_test__2labels_y = dataset.kdd_test_[\"is\"].copy() # For SVM\n",
    "    kdd_test__5labels_y = dataset.kdd_test_[\"type\"].copy() # For SVM\n",
    "\n",
    "    kdd_train_2labels.drop([\"label\", \"type\"], axis=1, inplace=True)\n",
    "    kdd_test_2labels.drop([\"label\", \"type\"], axis=1, inplace=True)\n",
    "    \n",
    "    kdd_train__2labels.drop([\"label\", \"type\"], axis=1, inplace=True)\n",
    "    kdd_test__2labels.drop([\"label\", \"type\"], axis=1, inplace=True)\n",
    "\n",
    "    kdd_train_5labels.drop([\"label\", \"is\"], axis=1, inplace=True)\n",
    "    kdd_test_5labels.drop([\"label\", \"is\"], axis=1, inplace=True)\n",
    "    \n",
    "    kdd_train__5labels.drop([\"label\", \"is\"], axis=1, inplace=True)\n",
    "    kdd_test__5labels.drop([\"label\", \"is\"], axis=1, inplace=True)\n",
    "    \n",
    "    kdd_train_2labels_y = kdd_train_2labels_y.map(lambda x: attack_codes_2labels[x])\n",
    "    kdd_test_2labels_y = kdd_test_2labels_y.map(lambda x: attack_codes_2labels[x])\n",
    "    \n",
    "    kdd_train__2labels_y = kdd_train__2labels_y.map(lambda x: attack_codes_2labels[x])\n",
    "    kdd_test__2labels_y = kdd_test__2labels_y.map(lambda x: attack_codes_2labels[x])\n",
    "    \n",
    "    kdd_train_5labels_y = kdd_train_5labels_y.map(lambda x: attack_codes_5labels[x])\n",
    "    kdd_test_5labels_y = kdd_test_5labels_y.map(lambda x: attack_codes_5labels[x])\n",
    "    \n",
    "    kdd_train__5labels_y = kdd_train__5labels_y.map(lambda x: attack_codes_5labels[x])\n",
    "    kdd_test__5labels_y = kdd_test__5labels_y.map(lambda x: attack_codes_5labels[x])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.849223Z",
     "start_time": "2017-06-19T17:09:48.843851Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preprocessing.kdd_train_2labels.columns.to_series().to_csv(\"dataset/columns_2labels.csv\")\n",
    "preprocessing.kdd_train_5labels.columns.to_series().to_csv(\"dataset/columns_5labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.878715Z",
     "start_time": "2017-06-19T17:09:48.851275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment',\n",
       "       'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised',\n",
       "       ...\n",
       "       'flag_RSTOS0', 'flag_RSTR', 'flag_S0', 'flag_S1', 'flag_S2', 'flag_S3',\n",
       "       'flag_SF', 'flag_SH', 'is_Attack', 'is_Normal'],\n",
       "      dtype='object', length=124)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_train_2labels.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.908926Z",
     "start_time": "2017-06-19T17:09:48.881127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125973, 124)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_train_2labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.934403Z",
     "start_time": "2017-06-19T17:09:48.910970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125973, 127)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_train_5labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.942563Z",
     "start_time": "2017-06-19T17:09:48.936602Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22544, 124)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_test_2labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.973014Z",
     "start_time": "2017-06-19T17:09:48.944817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22544, 127)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_test_5labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:48.998211Z",
     "start_time": "2017-06-19T17:09:48.975343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125973,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_train_2labels_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:49.011267Z",
     "start_time": "2017-06-19T17:09:49.000583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22544,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_test_2labels_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:49.033777Z",
     "start_time": "2017-06-19T17:09:49.015360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125973,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_train_5labels_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:49.056573Z",
     "start_time": "2017-06-19T17:09:49.035691Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22544,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.kdd_test_5labels_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:31:04.854474Z",
     "start_time": "2017-07-23T18:31:04.848771Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from pandas.plotting import andrews_curves\n",
    "from pandas.plotting import parallel_coordinates\n",
    "from sklearn import preprocessing as ps\n",
    "from pandas.plotting import radviz\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:49.453815Z",
     "start_time": "2017-06-19T17:09:49.058532Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = preprocessing.kdd_train_2labels.drop([\"is_Attack\", \"is_Normal\"], axis = 1)\n",
    "df_test = preprocessing.kdd_test_2labels.drop([\"is_Attack\", \"is_Normal\"], axis = 1)\n",
    "df_train = pd.concat([df_train, preprocessing.kdd_train_2labels_y], axis = 1)\n",
    "df_test = pd.concat([df_test, preprocessing.kdd_test_2labels_y], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:30:01.126420Z",
     "start_time": "2017-07-23T18:29:55.581344Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "model = TSNE(n_components=2, random_state=0)\n",
    "#np.set_printoptions(suppress=True)\n",
    "#sample = df_train.sample(int(df_train.shape[0]*.1)) # 10% of total data\n",
    "#sample.to_pickle(\"dataset/tsne_sample.pkl\")\n",
    "sample = pd.read_pickle(\"dataset/tsne_sample.pkl\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:31:53.071121Z",
     "start_time": "2017-07-23T18:31:53.027574Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_tsne = sample.iloc[:, :-1]\n",
    "y_tsne = sample.iloc[:, -1]\n",
    "\n",
    "\n",
    "from sklearn.decomposition import SparsePCA\n",
    "pca_analysis = SparsePCA(n_components=40)\n",
    "#x_tsne_pca = pca_analysis.fit_transform(x_tsne)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:31:55.229898Z",
     "start_time": "2017-07-23T18:31:55.222996Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pd.DataFrame(x_tsne_pca).to_pickle(\"dataset/tsne_pca_df.pkl\")\n",
    "x_tsne_pca = pd.read_pickle(\"dataset/tsne_pca_df.pkl\").values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:32:10.192126Z",
     "start_time": "2017-07-23T18:32:10.183052Z"
    }
   },
   "outputs": [],
   "source": [
    "x_tsne_pca_df = pd.DataFrame(x_tsne_pca)\n",
    "\n",
    "codes_to_attack = {1:\"Attack\", 0:\"Normal\"}\n",
    "\n",
    "y_tsne_cta = y_tsne.map(lambda x: codes_to_attack[x])\n",
    "x_tsne_pca_df['is'] = y_tsne_cta.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:35:50.852646Z",
     "start_time": "2017-06-19T17:33:02.833824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2b005c5c0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,3))\n",
    "\n",
    "andrews_curves(x_tsne_pca_df, \"is\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:50.653085Z",
     "start_time": "2017-06-19T17:09:50.647370Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df = model.fit_transform(x_tsne_pca) \n",
    "#df1 = model.fit_transform(df)\n",
    "#df2 = model.fit_transform(df1) \n",
    "#df3 = model.fit_transform(df2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:50.667074Z",
     "start_time": "2017-06-19T17:09:50.660528Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pd.DataFrame(df).to_pickle(\"dataset/tsne_df.pkl\")\n",
    "#pd.DataFrame(df1).to_pickle(\"dataset/tsne_df1.pkl\")\n",
    "#pd.DataFrame(df2).to_pickle(\"dataset/tsne_df2.pkl\")\n",
    "#pd.DataFrame(df3).to_pickle(\"dataset/tsne_df3.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:30:10.159694Z",
     "start_time": "2017-07-23T18:30:09.874555Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_pickle(\"dataset/tsne_df.pkl\").values\n",
    "df1 = pd.read_pickle(\"dataset/tsne_df1.pkl\").values\n",
    "df2 = pd.read_pickle(\"dataset/tsne_df2.pkl\").values\n",
    "df3 = pd.read_pickle(\"dataset/tsne_df3.pkl\").values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:52.811020Z",
     "start_time": "2017-06-19T17:09:50.800909Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd2a7ffc5f8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plt.figure(figsize=(15,8))\n",
    "\n",
    "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10,5))\n",
    "\n",
    "ax1.scatter(x = df[y_tsne==0,0], y = df[y_tsne==0,1], label = 'Normal')\n",
    "ax1.scatter(x = df[y_tsne==1,0], y = df[y_tsne==1,1], label = 'Attack')\n",
    "ax1.title.set_text(\"After 1000 epochs\")\n",
    "\n",
    "ax2.scatter(x = df1[y_tsne==0,0], y = df1[y_tsne==0,1], label = 'Normal')\n",
    "ax2.scatter(x = df1[y_tsne==1,0], y = df1[y_tsne==1,1], label = 'Attack')\n",
    "ax2.title.set_text(\"After 2000 epochs\")\n",
    "\n",
    "ax3.scatter(x = df2[y_tsne==0,0], y = df2[y_tsne==0,1], label = 'Normal')\n",
    "ax3.scatter(x = df2[y_tsne==1,0], y = df2[y_tsne==1,1], label = 'Attack')\n",
    "ax3.title.set_text(\"After 3000 epochs\")\n",
    "\n",
    "ax4.scatter(x = df3[y_tsne==0,0], y = df3[y_tsne==0,1], label = 'Normal')\n",
    "ax4.scatter(x = df3[y_tsne==1,0], y = df3[y_tsne==1,1], label = 'Attack')\n",
    "ax4.title.set_text(\"After 4000 epochs\")\n",
    "\n",
    "plt.subplots_adjust(wspace=0.05, hspace=0.18)\n",
    "ax1.legend(loc=0)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-23T18:35:52.209724Z",
     "start_time": "2017-07-23T18:35:51.682822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After 4000 epochs')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.scatter(x = df3[y_tsne==0,0], y = df3[y_tsne==0,1], label = 'Normal')\n",
    "plt.scatter(x = df3[y_tsne==1,0], y = df3[y_tsne==1,1], label = 'Attack')\n",
    "plt.title(\"After 4000 epochs\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:53.494112Z",
     "start_time": "2017-06-19T17:09:52.812844Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preprocessing.kdd_train_2labels.to_pickle(\"dataset/kdd_train_2labels.pkl\")\n",
    "preprocessing.kdd_train_2labels_y.to_pickle(\"dataset/kdd_train_2labels_y.pkl\")\n",
    "\n",
    "preprocessing.kdd_train_5labels.to_pickle(\"dataset/kdd_train_5labels.pkl\")\n",
    "preprocessing.kdd_train_5labels_y.to_pickle(\"dataset/kdd_train_5labels_y.pkl\")\n",
    "\n",
    "preprocessing.kdd_train__2labels.to_pickle(\"dataset/kdd_train__2labels.pkl\")\n",
    "preprocessing.kdd_train__2labels_y.to_pickle(\"dataset/kdd_train__2labels_y.pkl\")\n",
    "\n",
    "preprocessing.kdd_train__5labels.to_pickle(\"dataset/kdd_train__5labels.pkl\")\n",
    "preprocessing.kdd_train__5labels_y.to_pickle(\"dataset/kdd_train__5labels_y.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:53.559943Z",
     "start_time": "2017-06-19T17:09:53.496959Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "preprocessing.kdd_test_5labels_y.to_pickle(\"dataset/kdd_test_5labels_y.pkl\")\n",
    "\n",
    "preprocessing.kdd_test__5labels.to_pickle(\"dataset/kdd_test__5labels.pkl\")\n",
    "preprocessing.kdd_test__5labels_y.to_pickle(\"dataset/kdd_test__5labels_y.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T17:09:53.843631Z",
     "start_time": "2017-06-19T17:09:53.562060Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset.kdd_diff_level_train.to_pickle(\"dataset/kdd_diff_level_train.pkl\")\n",
    "dataset.kdd_diff_level_test.to_pickle(\"dataset/kdd_diff_level_test.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "_draft": {
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